Copperman J, Guenza M G
Department of Physics, University of Oregon, Eugene, Oregon 97403, USA.
Department of Chemistry and Institute of Theoretical Science, University of Oregon, Eugene, Oregon 97403, USA.
J Chem Phys. 2015 Dec 28;143(24):243131. doi: 10.1063/1.4935575.
The biological properties of proteins are uniquely determined by their structure and dynamics. A protein in solution populates a structural ensemble of metastable configurations around the global fold. From overall rotation to local fluctuations, the dynamics of proteins can cover several orders of magnitude in time scales. We propose a simulation-free coarse-grained approach which utilizes knowledge of the important metastable folded states of the protein to predict the protein dynamics. This approach is based upon the Langevin Equation for Protein Dynamics (LE4PD), a Langevin formalism in the coordinates of the protein backbone. The linear modes of this Langevin formalism organize the fluctuations of the protein, so that more extended dynamical cooperativity relates to increasing energy barriers to mode diffusion. The accuracy of the LE4PD is verified by analyzing the predicted dynamics across a set of seven different proteins for which both relaxation data and NMR solution structures are available. Using experimental NMR conformers as the input structural ensembles, LE4PD predicts quantitatively accurate results, with correlation coefficient ρ = 0.93 to NMR backbone relaxation measurements for the seven proteins. The NMR solution structure derived ensemble and predicted dynamical relaxation is compared with molecular dynamics simulation-derived structural ensembles and LE4PD predictions and is consistent in the time scale of the simulations. The use of the experimental NMR conformers frees the approach from computationally demanding simulations.
蛋白质的生物学特性由其结构和动力学唯一决定。溶液中的蛋白质在全局折叠周围的亚稳态构型结构集合中存在。从整体旋转到局部波动，蛋白质的动力学在时间尺度上可以覆盖几个数量级。我们提出了一种无需模拟的粗粒度方法，该方法利用蛋白质重要亚稳态折叠状态的知识来预测蛋白质动力学。这种方法基于蛋白质动力学的朗之万方程（LE4PD），这是一种在蛋白质主链坐标中的朗之万形式。这种朗之万形式的线性模式组织了蛋白质的波动，因此更广泛的动态协同性与模式扩散的能量障碍增加有关。通过分析一组七种不同蛋白质的预测动力学来验证LE4PD的准确性，这些蛋白质既有弛豫数据又有NMR溶液结构。使用实验性NMR构象作为输入结构集合，LE4PD预测出定量准确的结果，与这七种蛋白质的NMR主链弛豫测量的相关系数ρ = 0.93。将NMR溶液结构衍生的集合和预测的动态弛豫与分子动力学模拟衍生的结构集合和LE4PD预测进行比较，并且在模拟的时间尺度上是一致的。使用实验性NMR构象使该方法无需进行计算量大的模拟。
